Measuring the positions of multiple targets is key in many positioning applications. Utilizing compressed sensing theory, measurements using the sparse direct position determination (DPD) methods are more accurate than the two-step methods and the traditional DPD methods. Position measurement with mobile access points (APs) expands the network coverage, increases information throughput and reduces implementation costs, which motivates us to extend sparse DPD (SDPD) to the mobility-assisted positioning system. However, one of the most critical problems in mobility-assisted positioning is the model errors coming from the AP position and velocity uncertainty. The paper proposes a phase error compensation algorithm for the SDPD algorithm based on mobile APs. The algorithm makes two key technical contributions. First, a double iteration for -norm optimization is designed to compensate phase errors and recover target distribution maps simultaneously. Second, when the data collection is incomplete or irregular, the algorithm jointly integrates the global information of target distribution maps at different APs to improve the measurement accuracy. Experimental results indicate the effectiveness of the algorithm.

测量多个目标的位置是许多定位应用的关键。利用压缩感知理论，使用稀疏直接位置确定 (DPD) 方法的测量比两步法和传统 DPD 方法更准确。移动接入点 (AP) 的位置测量扩展了网络覆盖范围，增加了信息吞吐量并降低了实施成本，这促使我们将稀疏 DPD (SDPD) 扩展到移动辅助定位系统。然而，移动辅助定位中最关键的问题之一是来自 AP 位置和速度不确定性的模型误差。本文提出了一种基于移动AP的SDPD算法的相位误差补偿算法。该算法做出了两个关键的技术贡献。首先，双重迭代-范数优化旨在补偿相位误差并同时恢复目标分布图。其次，当数据采集不完整或不规则时，算法联合整合不同AP的目标分布图的全局信息，提高测量精度。实验结果表明了算法的有效性。